Análisis de modelos SOM - Frecuencia datos de entrada: mes

Modelo

• ID: 332
• Descripción:
• Frecuencia: mes
• Variables: fecha_cnt, tmax, tmin, precip
• Dimensiones del mapa: 10,10
• Iteraciones: 10000
• Parámetros adicionales:

source("../../lib/som-utils.R")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

source("../../lib/maps-utils.R")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Carga del modelo desde disco

mpr.set_base_path_analysis()
model <- mpr.load_model("som-332.rds.xz")
summary(model)

SOM of size 10x10 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 94881 objects.
Mean distance to the closest unit in the map: 0.181.

plot(model, type="changes")

Carga del dataset de entrada

df <- mpr.load_data("datos_mes.csv.xz")

df

summary(df)

 id_estacion           fecha             fecha_cnt           tmax      
 Length:94881       Length:94881       Min.   : 1.000   Min.   :-53.0  
 Class :character   Class :character   1st Qu.: 4.000   1st Qu.:148.0  
 Mode  :character   Mode  :character   Median : 6.000   Median :198.0  
                                       Mean   : 6.497   Mean   :200.2  
                                       3rd Qu.: 9.000   3rd Qu.:255.0  
                                       Max.   :12.000   Max.   :403.0  
      tmin             precip           nevada           prof_nieve      
 Min.   :-121.00   Min.   :  0.00   Min.   :0.000000   Min.   :   0.000  
 1st Qu.:  53.00   1st Qu.:  3.00   1st Qu.:0.000000   1st Qu.:   0.000  
 Median :  98.00   Median : 10.00   Median :0.000000   Median :   0.000  
 Mean   :  98.86   Mean   : 16.25   Mean   :0.000295   Mean   :   0.467  
 3rd Qu.: 148.00   3rd Qu.: 22.00   3rd Qu.:0.000000   3rd Qu.:   0.000  
 Max.   : 254.00   Max.   :422.00   Max.   :6.000000   Max.   :1834.000  
    longitud        latitud            altitud      
 Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:38.28   1st Qu.: -5.6417   1st Qu.:  42.0  
 Median :40.82   Median : -3.4500   Median : 247.0  
 Mean   :39.66   Mean   : -3.4350   Mean   : 418.5  
 3rd Qu.:42.08   3rd Qu.:  0.4914   3rd Qu.: 656.0  
 Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

Carga de los mapas

world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")

Mapa de densidad

plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)

Número de elementos en cada celda:

nb <- table(model$unit.classif)
print(nb)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
 725  819 1303 1760 1537 1739 1587 1498 1544 1843 1431 1268 1298 1111  793 1325 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
1306 1247 1528 1529 1231  568 1406 1342 1341 1123 1062  947  821 1215 1575 1451 
  33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
1484  995 1216  600  974 1151 1207  964 1090 1498  820 1046  842  669  813  513 
  49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64 
1299 1228 1430  765 1436 1035 1223  501  522 1018  971  985  963  243  775  499 
  65   66   67   68   69   70   71   72   73   74   75   76   77   78   79   80 
 648  153  284  798 1107 1316 1197 1013 1141  926  381  356  360  942 1450 1468 
  81   82   83   84   85   86   87   88   89   90   91   92   93   94   95   96 
 483  945  523  263   93  174  388  484 1094 1627  915  246  285  142   39    8 
  97   98   99  100 
  94  159  224 1107 

Comprobación de nodos vacíos:

dim_model <- 10*10;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
  print(paste("[Warning] Existen nodos vacíos: ", len_nb, "/", dim_model))
}

Mapa de distancia entre vecinos

plot(model, type="dist.neighbours", shape = "straight")

Influencia de las variables

model_colnames = c("fecha_cnt", "tmax", "tmin", "precip")
model_ncol = length(model_colnames)

Mapa de variables.

plot(model, shape = "straight")

Mapa de calor por variable

par(mfrow=c(3,4))
for (j in 1:model_ncol) {
  plot(model, type="property", property=getCodes(model,1)[,j],
    palette.name=mpr.coolBlueHotRed,
    main=model_colnames[j],
    cex=0.5, shape = "straight")
}

Correlación para cada columna del vector de nodos

if (!empty_nodes) {
  cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
  print(cor)
}

      fecha_cnt       tmax       tmin     precip
[1,] 0.90271970  0.3140273  0.3533610 0.01541472
[2,] 0.04017704 -0.8478021 -0.8286974 0.49004244

Representación de cada variable en un mapa de factores:

if (!empty_nodes) {
  par(mfrow=c(1,1))
  plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
  lines(c(-1,1),c(0,0))
  lines(c(0,0),c(-1,1))
  text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
  symbols(0,0,circles=1,inches=F,add=T)
}

Importancia de cada variable - varianza ponderada por el tamaño de la celda:

if (!empty_nodes) {
  sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
     {m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
     effectif=nb))
  print(sort(sigma2,decreasing=T))
}

fecha_cnt    precip      tmax      tmin 
0.9802178 0.9774398 0.9767485 0.9747406 

Clustering

if (!empty_nodes) {
  hac <- mpr.hac(model, nb)
}

Visualización de 3 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=3)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=3)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax          tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87   Min.   : 50.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:234   1st Qu.:131.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :263   Median :154.0   Median : 5.000   Median :0  
 Mean   : 7.117   Mean   :265   Mean   :155.6   Mean   : 9.059   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:294   3rd Qu.:180.0   3rd Qu.:13.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403   Max.   :254.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.18   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :39.88   Median : -3.6781   Median :  87.0  
 Mean   : 0.00331   Mean   :38.55   Mean   : -4.1504   Mean   : 283.3  
 3rd Qu.: 0.00000   3rd Qu.:41.65   3rd Qu.:  0.4483   3rd Qu.: 534.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin             precip     
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.0  
 1st Qu.: 2.000   1st Qu.:122.0   1st Qu.:  28.00   1st Qu.:  6.0  
 Median : 4.000   Median :156.0   Median :  58.00   Median : 15.0  
 Mean   : 5.995   Mean   :150.9   Mean   :  54.71   Mean   : 18.8  
 3rd Qu.:11.000   3rd Qu.:183.0   3rd Qu.:  84.00   3rd Qu.: 27.0  
 Max.   :12.000   Max.   :263.0   Max.   : 177.00   Max.   :116.0  
     nevada           prof_nieve           longitud        latitud        
 Min.   :0.000000   Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0.000000   1st Qu.:   0.0000   1st Qu.:39.49   1st Qu.: -5.2892  
 Median :0.000000   Median :   0.0000   Median :41.15   Median : -2.8194  
 Mean   :0.000541   Mean   :   0.6697   Mean   :40.48   Mean   : -2.8146  
 3rd Qu.:0.000000   3rd Qu.:   0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :6.000000   Max.   :1834.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  69.0  
 Median : 421.0  
 Mean   : 526.6  
 3rd Qu.: 775.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   :-42.0   Min.   :-115.0   Min.   : 62.00   Min.   :0  
 1st Qu.: 2.000   1st Qu.:116.0   1st Qu.:  48.5   1st Qu.: 78.00   1st Qu.:0  
 Median : 9.000   Median :141.0   Median :  74.0   Median : 90.00   Median :0  
 Mean   : 6.729   Mean   :144.4   Mean   :  75.1   Mean   : 98.56   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:178.0   3rd Qu.: 109.0   3rd Qu.:109.00   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   : 223.0   Max.   :422.00   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.95   1st Qu.: -8.411   1st Qu.:  35.0  
 Median :  0.000   Median :42.44   Median : -4.846   Median : 143.0  
 Mean   :  4.777   Mean   :41.42   Mean   : -4.782   Mean   : 403.2  
 3rd Qu.:  0.000   3rd Qu.:43.31   3rd Qu.: -1.787   3rd Qu.: 370.0  
 Max.   :892.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

Visualización de 4 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=4)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=4)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax          tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87   Min.   : 50.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:234   1st Qu.:131.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :263   Median :154.0   Median : 5.000   Median :0  
 Mean   : 7.117   Mean   :265   Mean   :155.6   Mean   : 9.059   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:294   3rd Qu.:180.0   3rd Qu.:13.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403   Max.   :254.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.18   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :39.88   Median : -3.6781   Median :  87.0  
 Mean   : 0.00331   Mean   :38.55   Mean   : -4.1504   Mean   : 283.3  
 3rd Qu.: 0.00000   3rd Qu.:41.65   3rd Qu.:  0.4483   3rd Qu.: 534.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin             precip     
 Min.   :1.000   Min.   :-53.0   Min.   :-121.00   Min.   : 0.00  
 1st Qu.:2.000   1st Qu.:123.0   1st Qu.:  25.00   1st Qu.: 6.00  
 Median :3.000   Median :155.0   Median :  54.00   Median :15.00  
 Mean   :2.819   Mean   :149.5   Mean   :  50.12   Mean   :18.03  
 3rd Qu.:4.000   3rd Qu.:180.0   3rd Qu.:  79.00   3rd Qu.:26.00  
 Max.   :8.000   Max.   :256.0   Max.   : 177.00   Max.   :93.00  
     nevada        prof_nieve           longitud        latitud        
 Min.   :0e+00   Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0e+00   1st Qu.:   0.0000   1st Qu.:39.48   1st Qu.: -5.3456  
 Median :0e+00   Median :   0.0000   Median :41.11   Median : -2.9056  
 Mean   :6e-04   Mean   :   0.9154   Mean   :40.47   Mean   : -2.8295  
 3rd Qu.:0e+00   3rd Qu.:   0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :6e+00   Max.   :1834.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  64.0  
 Median : 412.0  
 Mean   : 512.9  
 3rd Qu.: 750.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip   
 Min.   : 7.00   Min.   :-24.0   Min.   :-110.00   Min.   :  0  
 1st Qu.:10.00   1st Qu.:120.0   1st Qu.:  33.00   1st Qu.:  6  
 Median :11.00   Median :157.0   Median :  66.00   Median : 15  
 Mean   :10.99   Mean   :153.1   Mean   :  61.92   Mean   : 20  
 3rd Qu.:12.00   3rd Qu.:188.0   3rd Qu.:  94.00   3rd Qu.: 29  
 Max.   :12.00   Max.   :263.0   Max.   : 156.00   Max.   :116  
     nevada            prof_nieve          longitud        latitud        
 Min.   :0.0000000   Min.   :  0.0000   Min.   :27.92   Min.   :-17.7550  
 1st Qu.:0.0000000   1st Qu.:  0.0000   1st Qu.:39.49   1st Qu.: -5.2892  
 Median :0.0000000   Median :  0.0000   Median :41.15   Median : -2.7331  
 Mean   :0.0004474   Mean   :  0.2831   Mean   :40.50   Mean   : -2.7913  
 3rd Qu.:0.0000000   3rd Qu.:  0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :3.0000000   Max.   :382.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  71.0  
 Median : 445.0  
 Mean   : 548.2  
 3rd Qu.: 779.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   :-42.0   Min.   :-115.0   Min.   : 62.00   Min.   :0  
 1st Qu.: 2.000   1st Qu.:116.0   1st Qu.:  48.5   1st Qu.: 78.00   1st Qu.:0  
 Median : 9.000   Median :141.0   Median :  74.0   Median : 90.00   Median :0  
 Mean   : 6.729   Mean   :144.4   Mean   :  75.1   Mean   : 98.56   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:178.0   3rd Qu.: 109.0   3rd Qu.:109.00   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   : 223.0   Max.   :422.00   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.95   1st Qu.: -8.411   1st Qu.:  35.0  
 Median :  0.000   Median :42.44   Median : -4.846   Median : 143.0  
 Mean   :  4.777   Mean   :41.42   Mean   : -4.782   Mean   : 403.2  
 3rd Qu.:  0.000   3rd Qu.:43.31   3rd Qu.: -1.787   3rd Qu.: 370.0  
 Max.   :892.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

Visualización de 5 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=5)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=5)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax          tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87   Min.   : 50.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:234   1st Qu.:131.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :263   Median :154.0   Median : 5.000   Median :0  
 Mean   : 7.117   Mean   :265   Mean   :155.6   Mean   : 9.059   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:294   3rd Qu.:180.0   3rd Qu.:13.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403   Max.   :254.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.18   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :39.88   Median : -3.6781   Median :  87.0  
 Mean   : 0.00331   Mean   :38.55   Mean   : -4.1504   Mean   : 283.3  
 3rd Qu.: 0.00000   3rd Qu.:41.65   3rd Qu.:  0.4483   3rd Qu.: 534.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin             precip     
 Min.   :1.000   Min.   :-53.0   Min.   :-121.00   Min.   : 0.00  
 1st Qu.:2.000   1st Qu.:123.0   1st Qu.:  25.00   1st Qu.: 6.00  
 Median :3.000   Median :155.0   Median :  54.00   Median :15.00  
 Mean   :2.819   Mean   :149.5   Mean   :  50.12   Mean   :18.03  
 3rd Qu.:4.000   3rd Qu.:180.0   3rd Qu.:  79.00   3rd Qu.:26.00  
 Max.   :8.000   Max.   :256.0   Max.   : 177.00   Max.   :93.00  
     nevada        prof_nieve           longitud        latitud        
 Min.   :0e+00   Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0e+00   1st Qu.:   0.0000   1st Qu.:39.48   1st Qu.: -5.3456  
 Median :0e+00   Median :   0.0000   Median :41.11   Median : -2.9056  
 Mean   :6e-04   Mean   :   0.9154   Mean   :40.47   Mean   : -2.8295  
 3rd Qu.:0e+00   3rd Qu.:   0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :6e+00   Max.   :1834.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  64.0  
 Median : 412.0  
 Mean   : 512.9  
 3rd Qu.: 750.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip   
 Min.   : 7.00   Min.   :-24.0   Min.   :-110.00   Min.   :  0  
 1st Qu.:10.00   1st Qu.:120.0   1st Qu.:  33.00   1st Qu.:  6  
 Median :11.00   Median :157.0   Median :  66.00   Median : 15  
 Mean   :10.99   Mean   :153.1   Mean   :  61.92   Mean   : 20  
 3rd Qu.:12.00   3rd Qu.:188.0   3rd Qu.:  94.00   3rd Qu.: 29  
 Max.   :12.00   Max.   :263.0   Max.   : 156.00   Max.   :116  
     nevada            prof_nieve          longitud        latitud        
 Min.   :0.0000000   Min.   :  0.0000   Min.   :27.92   Min.   :-17.7550  
 1st Qu.:0.0000000   1st Qu.:  0.0000   1st Qu.:39.49   1st Qu.: -5.2892  
 Median :0.0000000   Median :  0.0000   Median :41.15   Median : -2.7331  
 Mean   :0.0004474   Mean   :  0.2831   Mean   :40.50   Mean   : -2.7913  
 3rd Qu.:0.0000000   3rd Qu.:  0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :3.0000000   Max.   :382.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  71.0  
 Median : 445.0  
 Mean   : 548.2  
 3rd Qu.: 779.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-42.0   Min.   :-115.00   Min.   : 62.00  
 1st Qu.: 2.000   1st Qu.:116.0   1st Qu.:  48.50   1st Qu.: 78.00  
 Median : 9.000   Median :141.0   Median :  74.00   Median : 90.00  
 Mean   : 6.731   Mean   :144.4   Mean   :  75.03   Mean   : 97.66  
 3rd Qu.:11.000   3rd Qu.:178.0   3rd Qu.: 109.00   3rd Qu.:109.00  
 Max.   :12.000   Max.   :331.0   Max.   : 223.00   Max.   :259.00  
     nevada    prof_nieve         longitud        latitud       
 Min.   :0   Min.   :  0.000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.:  0.000   1st Qu.:40.95   1st Qu.: -8.411  
 Median :0   Median :  0.000   Median :42.44   Median : -4.488  
 Mean   :0   Mean   :  4.796   Mean   :41.42   Mean   : -4.773  
 3rd Qu.:0   3rd Qu.:  0.000   3rd Qu.:43.31   3rd Qu.: -1.787  
 Max.   :0   Max.   :892.000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  35.0  
 Median : 143.0  
 Mean   : 402.8  
 3rd Qu.: 370.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : 37.0   Min.   :-11.00   Min.   :279.0   Min.   :0  
 1st Qu.: 2.750   1st Qu.:108.2   1st Qu.: 49.75   1st Qu.:294.2   1st Qu.:0  
 Median : 5.500   Median :127.0   Median : 78.50   Median :307.0   Median :0  
 Mean   : 6.125   Mean   :157.5   Mean   : 90.75   Mean   :323.1   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:194.0   3rd Qu.:137.25   3rd Qu.:332.8   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :204.00   Max.   :422.0   Max.   :0  
   prof_nieve    longitud        latitud           altitud      
 Min.   :0    Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:0    1st Qu.:41.42   1st Qu.: -8.649   1st Qu.:  19.0  
 Median :0    Median :42.33   Median : -8.517   Median : 262.0  
 Mean   :0    Mean   :40.50   Mean   : -7.103   Mean   : 493.2  
 3rd Qu.:0    3rd Qu.:42.54   3rd Qu.: -2.827   3rd Qu.: 437.2  
 Max.   :0    Max.   :43.36   Max.   : -1.033   Max.   :2371.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

Visualización de 6 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=6)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=6)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax          tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87   Min.   : 50.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:234   1st Qu.:131.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :263   Median :154.0   Median : 5.000   Median :0  
 Mean   : 7.117   Mean   :265   Mean   :155.6   Mean   : 9.059   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:294   3rd Qu.:180.0   3rd Qu.:13.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403   Max.   :254.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.18   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :39.88   Median : -3.6781   Median :  87.0  
 Mean   : 0.00331   Mean   :38.55   Mean   : -4.1504   Mean   : 283.3  
 3rd Qu.: 0.00000   3rd Qu.:41.65   3rd Qu.:  0.4483   3rd Qu.: 534.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin             precip     
 Min.   :1.000   Min.   :-53.0   Min.   :-121.00   Min.   : 0.00  
 1st Qu.:2.000   1st Qu.:123.0   1st Qu.:  25.00   1st Qu.: 6.00  
 Median :3.000   Median :155.0   Median :  54.00   Median :15.00  
 Mean   :2.819   Mean   :149.5   Mean   :  50.12   Mean   :18.03  
 3rd Qu.:4.000   3rd Qu.:180.0   3rd Qu.:  79.00   3rd Qu.:26.00  
 Max.   :8.000   Max.   :256.0   Max.   : 177.00   Max.   :93.00  
     nevada        prof_nieve           longitud        latitud        
 Min.   :0e+00   Min.   :   0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0e+00   1st Qu.:   0.0000   1st Qu.:39.48   1st Qu.: -5.3456  
 Median :0e+00   Median :   0.0000   Median :41.11   Median : -2.9056  
 Mean   :6e-04   Mean   :   0.9154   Mean   :40.47   Mean   : -2.8295  
 3rd Qu.:0e+00   3rd Qu.:   0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :6e+00   Max.   :1834.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  64.0  
 Median : 412.0  
 Mean   : 512.9  
 3rd Qu.: 750.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip   
 Min.   : 7.00   Min.   :-24.0   Min.   :-110.00   Min.   :  0  
 1st Qu.:10.00   1st Qu.:120.0   1st Qu.:  33.00   1st Qu.:  6  
 Median :11.00   Median :157.0   Median :  66.00   Median : 15  
 Mean   :10.99   Mean   :153.1   Mean   :  61.92   Mean   : 20  
 3rd Qu.:12.00   3rd Qu.:188.0   3rd Qu.:  94.00   3rd Qu.: 29  
 Max.   :12.00   Max.   :263.0   Max.   : 156.00   Max.   :116  
     nevada            prof_nieve          longitud        latitud        
 Min.   :0.0000000   Min.   :  0.0000   Min.   :27.92   Min.   :-17.7550  
 1st Qu.:0.0000000   1st Qu.:  0.0000   1st Qu.:39.49   1st Qu.: -5.2892  
 Median :0.0000000   Median :  0.0000   Median :41.15   Median : -2.7331  
 Mean   :0.0004474   Mean   :  0.2831   Mean   :40.50   Mean   : -2.7913  
 3rd Qu.:0.0000000   3rd Qu.:  0.0000   3rd Qu.:42.33   3rd Qu.:  0.4942  
 Max.   :3.0000000   Max.   :382.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  71.0  
 Median : 445.0  
 Mean   : 548.2  
 3rd Qu.: 779.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   :-42.0   Min.   :-115.0   Min.   : 62.00   Min.   :0  
 1st Qu.: 2.000   1st Qu.:117.0   1st Qu.:  49.0   1st Qu.: 77.00   1st Qu.:0  
 Median : 9.000   Median :143.0   Median :  75.0   Median : 87.00   Median :0  
 Mean   : 6.747   Mean   :146.2   Mean   :  76.1   Mean   : 89.87   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:183.0   3rd Qu.: 111.0   3rd Qu.:101.00   3rd Qu.:0  
 Max.   :12.000   Max.   :326.0   Max.   : 219.0   Max.   :155.00   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -8.411   1st Qu.:  34.0  
 Median :  0.000   Median :42.44   Median : -4.010   Median : 127.0  
 Mean   :  4.849   Mean   :41.44   Mean   : -4.533   Mean   : 394.2  
 3rd Qu.:  0.000   3rd Qu.:43.31   3rd Qu.: -1.787   3rd Qu.: 370.0  
 Max.   :892.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :121.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:110.2   1st Qu.: 48.00   1st Qu.:137.0   1st Qu.:0  
 Median : 7.500   Median :128.0   Median : 67.00   Median :148.0   Median :0  
 Mean   : 6.611   Mean   :130.0   Mean   : 66.76   Mean   :158.1   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:150.0   3rd Qu.: 87.00   3rd Qu.:171.0   3rd Qu.:0  
 Max.   :12.000   Max.   :331.0   Max.   :223.00   Max.   :259.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:41.69   1st Qu.: -8.624   1st Qu.: 100.5  
 Median :  0.000   Median :42.43   Median : -8.411   Median : 261.0  
 Mean   :  4.385   Mean   :41.25   Mean   : -6.641   Mean   : 470.0  
 3rd Qu.:  0.000   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.000   Max.   :43.49   Max.   :  2.827   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : 37.0   Min.   :-11.00   Min.   :279.0   Min.   :0  
 1st Qu.: 2.750   1st Qu.:108.2   1st Qu.: 49.75   1st Qu.:294.2   1st Qu.:0  
 Median : 5.500   Median :127.0   Median : 78.50   Median :307.0   Median :0  
 Mean   : 6.125   Mean   :157.5   Mean   : 90.75   Mean   :323.1   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:194.0   3rd Qu.:137.25   3rd Qu.:332.8   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :204.00   Max.   :422.0   Max.   :0  
   prof_nieve    longitud        latitud           altitud      
 Min.   :0    Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:0    1st Qu.:41.42   1st Qu.: -8.649   1st Qu.:  19.0  
 Median :0    Median :42.33   Median : -8.517   Median : 262.0  
 Mean   :0    Mean   :40.50   Mean   : -7.103   Mean   : 493.2  
 3rd Qu.:0    3rd Qu.:42.54   3rd Qu.: -2.827   3rd Qu.: 437.2  
 Max.   :0    Max.   :43.36   Max.   : -1.033   Max.   :2371.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

Visualización de 8 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=8)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=8)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax          tmin           precip           nevada 
 Min.   : 1.000   Min.   : 87   Min.   : 50.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:234   1st Qu.:131.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :263   Median :154.0   Median : 5.000   Median :0  
 Mean   : 7.117   Mean   :265   Mean   :155.6   Mean   : 9.059   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:294   3rd Qu.:180.0   3rd Qu.:13.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403   Max.   :254.0   Max.   :73.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.18   1st Qu.: -6.0442   1st Qu.:  27.1  
 Median : 0.00000   Median :39.88   Median : -3.6781   Median :  87.0  
 Mean   : 0.00331   Mean   :38.55   Mean   : -4.1504   Mean   : 283.3  
 3rd Qu.: 0.00000   3rd Qu.:41.65   3rd Qu.:  0.4483   3rd Qu.: 534.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax            tmin            precip     
 Min.   :1.000   Min.   : 71.0   Min.   :-36.00   Min.   : 0.00  
 1st Qu.:2.000   1st Qu.:150.0   1st Qu.: 50.00   1st Qu.: 6.00  
 Median :3.000   Median :169.0   Median : 69.00   Median :14.00  
 Mean   :3.134   Mean   :170.1   Mean   : 67.53   Mean   :17.78  
 3rd Qu.:4.000   3rd Qu.:189.0   3rd Qu.: 86.00   3rd Qu.:25.00  
 Max.   :8.000   Max.   :256.0   Max.   :177.00   Max.   :93.00  
     nevada            prof_nieve         longitud        latitud        
 Min.   :0.0000000   Min.   : 0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0.0000000   1st Qu.: 0.0000   1st Qu.:38.88   1st Qu.: -5.6156  
 Median :0.0000000   Median : 0.0000   Median :40.95   Median : -2.9553  
 Mean   :0.0001772   Mean   : 0.0558   Mean   :40.21   Mean   : -2.9190  
 3rd Qu.:0.0000000   3rd Qu.: 0.0000   3rd Qu.:42.24   3rd Qu.:  0.4914  
 Max.   :2.0000000   Max.   :65.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  43.0  
 Median : 176.0  
 Mean   : 362.7  
 3rd Qu.: 611.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin             precip     
 Min.   : 7.00   Min.   :-24.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.:10.00   1st Qu.:121.0   1st Qu.:  29.00   1st Qu.: 4.00  
 Median :11.00   Median :164.0   Median :  64.00   Median :10.00  
 Mean   :10.97   Mean   :156.7   Mean   :  60.15   Mean   :11.61  
 3rd Qu.:12.00   3rd Qu.:194.0   3rd Qu.:  94.00   3rd Qu.:18.00  
 Max.   :12.00   Max.   :263.0   Max.   : 150.00   Max.   :46.00  
     nevada            prof_nieve         longitud        latitud        
 Min.   :0.0000000   Min.   :  0.000   Min.   :27.92   Min.   :-17.7550  
 1st Qu.:0.0000000   1st Qu.:  0.000   1st Qu.:39.47   1st Qu.: -4.6800  
 Median :0.0000000   Median :  0.000   Median :40.95   Median : -2.3308  
 Mean   :0.0006036   Mean   :  0.185   Mean   :40.27   Mean   : -2.5458  
 3rd Qu.:0.0000000   3rd Qu.:  0.000   3rd Qu.:41.98   3rd Qu.:  0.5706  
 Max.   :3.0000000   Max.   :338.000   Max.   :43.57   Max.   :  4.2156  
    altitud    
 Min.   :   1  
 1st Qu.:  81  
 Median : 515  
 Mean   : 555  
 3rd Qu.: 788  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin              precip     
 Min.   :1.000   Min.   :-53.00   Min.   :-121.000   Min.   : 0.00  
 1st Qu.:1.000   1st Qu.: 82.00   1st Qu.:  -9.000   1st Qu.: 7.00  
 Median :2.000   Median :103.00   Median :  10.000   Median :16.00  
 Mean   :2.033   Mean   : 97.97   Mean   :   6.764   Mean   :18.66  
 3rd Qu.:3.000   3rd Qu.:120.00   3rd Qu.:  26.000   3rd Qu.:28.00  
 Max.   :7.000   Max.   :192.00   Max.   :  71.000   Max.   :72.00  
     nevada           prof_nieve          longitud        latitud       
 Min.   :0.000000   Min.   :   0.000   Min.   :28.31   Min.   :-16.499  
 1st Qu.:0.000000   1st Qu.:   0.000   1st Qu.:40.70   1st Qu.: -4.127  
 Median :0.000000   Median :   0.000   Median :41.65   Median : -2.654  
 Mean   :0.001655   Mean   :   3.057   Mean   :41.10   Mean   : -2.607  
 3rd Qu.:0.000000   3rd Qu.:   0.000   3rd Qu.:42.38   3rd Qu.:  0.595  
 Max.   :6.000000   Max.   :1834.000   Max.   :43.57   Max.   :  4.216  
    altitud    
 Min.   :   1  
 1st Qu.: 515  
 Median : 735  
 Mean   : 887  
 3rd Qu.:1005  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax            tmin            precip           nevada 
 Min.   : 8.00   Min.   :-15.0   Min.   :-75.00   Min.   : 21.00   Min.   :0  
 1st Qu.:11.00   1st Qu.:119.0   1st Qu.: 44.00   1st Qu.: 33.00   1st Qu.:0  
 Median :11.00   Median :146.0   Median : 69.00   Median : 41.00   Median :0  
 Mean   :11.05   Mean   :142.7   Mean   : 66.97   Mean   : 44.03   Mean   :0  
 3rd Qu.:12.00   3rd Qu.:172.0   3rd Qu.: 92.00   3rd Qu.: 53.00   3rd Qu.:0  
 Max.   :12.00   Max.   :263.0   Max.   :156.00   Max.   :116.00   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :  0.0000   Min.   :27.92   Min.   :-17.7550   Min.   :   1.0  
 1st Qu.:  0.0000   1st Qu.:40.47   1st Qu.: -5.8728   1st Qu.:  58.0  
 Median :  0.0000   Median :42.14   Median : -3.7892   Median : 336.0  
 Mean   :  0.5642   Mean   :41.15   Mean   : -3.4944   Mean   : 528.7  
 3rd Qu.:  0.0000   3rd Qu.:43.12   3rd Qu.:  0.3264   3rd Qu.: 667.0  
 Max.   :382.0000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   :-42.0   Min.   :-115.0   Min.   : 62.00   Min.   :0  
 1st Qu.: 2.000   1st Qu.:117.0   1st Qu.:  49.0   1st Qu.: 77.00   1st Qu.:0  
 Median : 9.000   Median :143.0   Median :  75.0   Median : 87.00   Median :0  
 Mean   : 6.747   Mean   :146.2   Mean   :  76.1   Mean   : 89.87   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:183.0   3rd Qu.: 111.0   3rd Qu.:101.00   3rd Qu.:0  
 Max.   :12.000   Max.   :326.0   Max.   : 219.0   Max.   :155.00   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:40.82   1st Qu.: -8.411   1st Qu.:  34.0  
 Median :  0.000   Median :42.44   Median : -4.010   Median : 127.0  
 Mean   :  4.849   Mean   :41.44   Mean   : -4.533   Mean   : 394.2  
 3rd Qu.:  0.000   3rd Qu.:43.31   3rd Qu.: -1.787   3rd Qu.: 370.0  
 Max.   :892.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :121.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:110.2   1st Qu.: 48.00   1st Qu.:137.0   1st Qu.:0  
 Median : 7.500   Median :128.0   Median : 67.00   Median :148.0   Median :0  
 Mean   : 6.611   Mean   :130.0   Mean   : 66.76   Mean   :158.1   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:150.0   3rd Qu.: 87.00   3rd Qu.:171.0   3rd Qu.:0  
 Max.   :12.000   Max.   :331.0   Max.   :223.00   Max.   :259.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:41.69   1st Qu.: -8.624   1st Qu.: 100.5  
 Median :  0.000   Median :42.43   Median : -8.411   Median : 261.0  
 Mean   :  4.385   Mean   :41.25   Mean   : -6.641   Mean   : 470.0  
 3rd Qu.:  0.000   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.000   Max.   :43.49   Max.   :  2.827   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : 37.0   Min.   :-11.00   Min.   :279.0   Min.   :0  
 1st Qu.: 2.750   1st Qu.:108.2   1st Qu.: 49.75   1st Qu.:294.2   1st Qu.:0  
 Median : 5.500   Median :127.0   Median : 78.50   Median :307.0   Median :0  
 Mean   : 6.125   Mean   :157.5   Mean   : 90.75   Mean   :323.1   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:194.0   3rd Qu.:137.25   3rd Qu.:332.8   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :204.00   Max.   :422.0   Max.   :0  
   prof_nieve    longitud        latitud           altitud      
 Min.   :0    Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:0    1st Qu.:41.42   1st Qu.: -8.649   1st Qu.:  19.0  
 Median :0    Median :42.33   Median : -8.517   Median : 262.0  
 Mean   :0    Mean   :40.50   Mean   : -7.103   Mean   : 493.2  
 3rd Qu.:0    3rd Qu.:42.54   3rd Qu.: -2.827   3rd Qu.: 437.2  
 Max.   :0    Max.   :43.36   Max.   : -1.033   Max.   :2371.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

Visualización de 10 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=10)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=10)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)
  df.cluster09 <- subset(df, cluster==9)
  df.cluster10 <- subset(df, cluster==10)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin           precip          nevada 
 Min.   : 1.000   Min.   : 87.0   Min.   : 50.0   Min.   : 0.00   Min.   :0  
 1st Qu.: 5.000   1st Qu.:213.0   1st Qu.:116.0   1st Qu.: 4.00   1st Qu.:0  
 Median : 5.000   Median :231.0   Median :133.0   Median :10.00   Median :0  
 Mean   : 5.586   Mean   :231.8   Mean   :134.1   Mean   :15.74   Mean   :0  
 3rd Qu.: 6.000   3rd Qu.:249.0   3rd Qu.:153.0   3rd Qu.:26.00   3rd Qu.:0  
 Max.   :12.000   Max.   :352.0   Max.   :244.0   Max.   :73.00   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:36.85   1st Qu.: -6.3392   1st Qu.:  25.0  
 Median : 0.00000   Median :40.38   Median : -3.7881   Median :  64.0  
 Mean   : 0.00677   Mean   :38.60   Mean   : -4.4900   Mean   : 237.1  
 3rd Qu.: 0.00000   3rd Qu.:42.11   3rd Qu.:  0.3994   3rd Qu.: 412.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 4.000   Min.   :181.0   Min.   : 54.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 7.000   1st Qu.:258.0   1st Qu.:144.0   1st Qu.: 0.000   1st Qu.:0  
 Median : 8.000   Median :282.0   Median :167.0   Median : 3.000   Median :0  
 Mean   : 7.924   Mean   :282.4   Mean   :166.8   Mean   : 5.542   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:306.0   3rd Qu.:191.0   3rd Qu.: 9.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403.0   Max.   :254.0   Max.   :31.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.26   1st Qu.: -5.8728   1st Qu.:  32.0  
 Median : 0.00000   Median :39.85   Median : -3.6325   Median :  91.0  
 Mean   : 0.00148   Mean   :38.53   Mean   : -3.9716   Mean   : 307.7  
 3rd Qu.: 0.00000   3rd Qu.:41.48   3rd Qu.:  0.4483   3rd Qu.: 582.0  
 Max.   :35.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2371.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt          tmax            tmin            precip     
 Min.   :1.000   Min.   : 71.0   Min.   :-36.00   Min.   : 0.00  
 1st Qu.:2.000   1st Qu.:150.0   1st Qu.: 50.00   1st Qu.: 6.00  
 Median :3.000   Median :169.0   Median : 69.00   Median :14.00  
 Mean   :3.134   Mean   :170.1   Mean   : 67.53   Mean   :17.78  
 3rd Qu.:4.000   3rd Qu.:189.0   3rd Qu.: 86.00   3rd Qu.:25.00  
 Max.   :8.000   Max.   :256.0   Max.   :177.00   Max.   :93.00  
     nevada            prof_nieve         longitud        latitud        
 Min.   :0.0000000   Min.   : 0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0.0000000   1st Qu.: 0.0000   1st Qu.:38.88   1st Qu.: -5.6156  
 Median :0.0000000   Median : 0.0000   Median :40.95   Median : -2.9553  
 Mean   :0.0001772   Mean   : 0.0558   Mean   :40.21   Mean   : -2.9190  
 3rd Qu.:0.0000000   3rd Qu.: 0.0000   3rd Qu.:42.24   3rd Qu.:  0.4914  
 Max.   :2.0000000   Max.   :65.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  43.0  
 Median : 176.0  
 Mean   : 362.7  
 3rd Qu.: 611.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax            tmin             precip     
 Min.   : 7.00   Min.   :-24.0   Min.   :-110.00   Min.   : 0.00  
 1st Qu.:10.00   1st Qu.:121.0   1st Qu.:  29.00   1st Qu.: 4.00  
 Median :11.00   Median :164.0   Median :  64.00   Median :10.00  
 Mean   :10.97   Mean   :156.7   Mean   :  60.15   Mean   :11.61  
 3rd Qu.:12.00   3rd Qu.:194.0   3rd Qu.:  94.00   3rd Qu.:18.00  
 Max.   :12.00   Max.   :263.0   Max.   : 150.00   Max.   :46.00  
     nevada            prof_nieve         longitud        latitud        
 Min.   :0.0000000   Min.   :  0.000   Min.   :27.92   Min.   :-17.7550  
 1st Qu.:0.0000000   1st Qu.:  0.000   1st Qu.:39.47   1st Qu.: -4.6800  
 Median :0.0000000   Median :  0.000   Median :40.95   Median : -2.3308  
 Mean   :0.0006036   Mean   :  0.185   Mean   :40.27   Mean   : -2.5458  
 3rd Qu.:0.0000000   3rd Qu.:  0.000   3rd Qu.:41.98   3rd Qu.:  0.5706  
 Max.   :3.0000000   Max.   :338.000   Max.   :43.57   Max.   :  4.2156  
    altitud    
 Min.   :   1  
 1st Qu.:  81  
 Median : 515  
 Mean   : 555  
 3rd Qu.: 788  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt          tmax             tmin              precip     
 Min.   :1.000   Min.   :-53.00   Min.   :-121.000   Min.   : 0.00  
 1st Qu.:1.000   1st Qu.: 82.00   1st Qu.:  -9.000   1st Qu.: 7.00  
 Median :2.000   Median :103.00   Median :  10.000   Median :16.00  
 Mean   :2.033   Mean   : 97.97   Mean   :   6.764   Mean   :18.66  
 3rd Qu.:3.000   3rd Qu.:120.00   3rd Qu.:  26.000   3rd Qu.:28.00  
 Max.   :7.000   Max.   :192.00   Max.   :  71.000   Max.   :72.00  
     nevada           prof_nieve          longitud        latitud       
 Min.   :0.000000   Min.   :   0.000   Min.   :28.31   Min.   :-16.499  
 1st Qu.:0.000000   1st Qu.:   0.000   1st Qu.:40.70   1st Qu.: -4.127  
 Median :0.000000   Median :   0.000   Median :41.65   Median : -2.654  
 Mean   :0.001655   Mean   :   3.057   Mean   :41.10   Mean   : -2.607  
 3rd Qu.:0.000000   3rd Qu.:   0.000   3rd Qu.:42.38   3rd Qu.:  0.595  
 Max.   :6.000000   Max.   :1834.000   Max.   :43.57   Max.   :  4.216  
    altitud    
 Min.   :   1  
 1st Qu.: 515  
 Median : 735  
 Mean   : 887  
 3rd Qu.:1005  
 Max.   :2535  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt          tmax            tmin            precip           nevada 
 Min.   : 8.00   Min.   :-15.0   Min.   :-75.00   Min.   : 21.00   Min.   :0  
 1st Qu.:11.00   1st Qu.:119.0   1st Qu.: 44.00   1st Qu.: 33.00   1st Qu.:0  
 Median :11.00   Median :146.0   Median : 69.00   Median : 41.00   Median :0  
 Mean   :11.05   Mean   :142.7   Mean   : 66.97   Mean   : 44.03   Mean   :0  
 3rd Qu.:12.00   3rd Qu.:172.0   3rd Qu.: 92.00   3rd Qu.: 53.00   3rd Qu.:0  
 Max.   :12.00   Max.   :263.0   Max.   :156.00   Max.   :116.00   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :  0.0000   Min.   :27.92   Min.   :-17.7550   Min.   :   1.0  
 1st Qu.:  0.0000   1st Qu.:40.47   1st Qu.: -5.8728   1st Qu.:  58.0  
 Median :  0.0000   Median :42.14   Median : -3.7892   Median : 336.0  
 Mean   :  0.5642   Mean   :41.15   Mean   : -3.4944   Mean   : 528.7  
 3rd Qu.:  0.0000   3rd Qu.:43.12   3rd Qu.:  0.3264   3rd Qu.: 667.0  
 Max.   :382.0000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt          tmax            tmin           precip           nevada 
 Min.   : 5.00   Min.   :  3.0   Min.   :-48.0   Min.   : 66.00   Min.   :0  
 1st Qu.:10.00   1st Qu.:144.0   1st Qu.: 78.0   1st Qu.: 80.00   1st Qu.:0  
 Median :11.00   Median :174.0   Median :106.0   Median : 90.00   Median :0  
 Mean   :10.44   Mean   :177.2   Mean   :105.7   Mean   : 93.89   Mean   :0  
 3rd Qu.:11.00   3rd Qu.:211.0   3rd Qu.:133.0   3rd Qu.:105.50   3rd Qu.:0  
 Max.   :12.00   Max.   :326.0   Max.   :219.0   Max.   :155.00   Max.   :0  
   prof_nieve          longitud        latitud           altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:39.99   1st Qu.: -8.411   1st Qu.:  22.0  
 Median : 0.00000   Median :42.43   Median : -4.010   Median :  87.0  
 Mean   : 0.08342   Mean   :41.11   Mean   : -4.418   Mean   : 215.8  
 3rd Qu.: 0.00000   3rd Qu.:43.31   3rd Qu.: -1.787   3rd Qu.: 261.0  
 Max.   :14.00000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   :1.000   Min.   :-42.0   Min.   :-115.00   Min.   : 62.00   Min.   :0  
 1st Qu.:1.000   1st Qu.: 97.0   1st Qu.:  26.00   1st Qu.: 73.00   1st Qu.:0  
 Median :2.000   Median :118.0   Median :  49.00   Median : 82.00   Median :0  
 Mean   :2.179   Mean   :107.8   Mean   :  39.54   Mean   : 84.91   Mean   :0  
 3rd Qu.:3.000   3rd Qu.:137.0   3rd Qu.:  66.00   3rd Qu.: 95.00   3rd Qu.:0  
 Max.   :6.000   Max.   :218.0   Max.   : 150.00   Max.   :126.00   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   :  0.00   Min.   :27.92   Min.   :-17.755   Min.   :   1.0  
 1st Qu.:  0.00   1st Qu.:42.06   1st Qu.: -8.411   1st Qu.:  64.0  
 Median :  0.00   Median :42.56   Median : -4.010   Median : 261.0  
 Mean   : 10.74   Mean   :41.85   Mean   : -4.675   Mean   : 614.6  
 3rd Qu.:  0.00   3rd Qu.:43.31   3rd Qu.: -2.039   3rd Qu.: 534.0  
 Max.   :892.00   Max.   :43.57   Max.   :  2.827   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster09)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :121.0   Min.   :0  
 1st Qu.: 2.000   1st Qu.:110.2   1st Qu.: 48.00   1st Qu.:137.0   1st Qu.:0  
 Median : 7.500   Median :128.0   Median : 67.00   Median :148.0   Median :0  
 Mean   : 6.611   Mean   :130.0   Mean   : 66.76   Mean   :158.1   Mean   :0  
 3rd Qu.:11.000   3rd Qu.:150.0   3rd Qu.: 87.00   3rd Qu.:171.0   3rd Qu.:0  
 Max.   :12.000   Max.   :331.0   Max.   :223.00   Max.   :259.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud      
 Min.   :  0.000   Min.   :27.82   Min.   :-17.889   Min.   :   1.0  
 1st Qu.:  0.000   1st Qu.:41.69   1st Qu.: -8.624   1st Qu.: 100.5  
 Median :  0.000   Median :42.43   Median : -8.411   Median : 261.0  
 Mean   :  4.385   Mean   :41.25   Mean   : -6.641   Mean   : 470.0  
 3rd Qu.:  0.000   3rd Qu.:42.89   3rd Qu.: -4.010   3rd Qu.: 370.0  
 Max.   :607.000   Max.   :43.49   Max.   :  2.827   Max.   :2400.0  

if (!empty_nodes) summary(df.cluster10)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : 37.0   Min.   :-11.00   Min.   :279.0   Min.   :0  
 1st Qu.: 2.750   1st Qu.:108.2   1st Qu.: 49.75   1st Qu.:294.2   1st Qu.:0  
 Median : 5.500   Median :127.0   Median : 78.50   Median :307.0   Median :0  
 Mean   : 6.125   Mean   :157.5   Mean   : 90.75   Mean   :323.1   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:194.0   3rd Qu.:137.25   3rd Qu.:332.8   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :204.00   Max.   :422.0   Max.   :0  
   prof_nieve    longitud        latitud           altitud      
 Min.   :0    Min.   :28.31   Min.   :-16.499   Min.   :   4.0  
 1st Qu.:0    1st Qu.:41.42   1st Qu.: -8.649   1st Qu.:  19.0  
 Median :0    Median :42.33   Median : -8.517   Median : 262.0  
 Mean   :0    Mean   :40.50   Mean   : -7.103   Mean   : 493.2  
 3rd Qu.:0    3rd Qu.:42.54   3rd Qu.: -2.827   3rd Qu.: 437.2  
 Max.   :0    Max.   :43.36   Max.   : -1.033   Max.   :2371.0  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.hist(df.cluster09)

if (!empty_nodes) mpr.hist(df.cluster10)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster09)

if (!empty_nodes) mpr.boxplot(df.cluster10)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
  df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
  df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)